A simple yet efficient algorithm for multiple kernel learning under elastic-net constraints
This work addresses a specific computational bottleneck in machine learning for researchers and practitioners, but it is incremental as it builds on existing MKL methods with minor enhancements.
The paper tackles the problem of multiple kernel learning with elastic-net constraints by introducing an algorithm that improves time and space complexity compared to existing approaches, achieving efficient performance without reliance on external libraries beyond a standard SVM solver.
This papers introduces an algorithm for the solution of multiple kernel learning (MKL) problems with elastic-net constraints on the kernel weights. The algorithm compares very favourably in terms of time and space complexity to existing approaches and can be implemented with simple code that does not rely on external libraries (except a conventional SVM solver).